We define a new method for global optimization, the Homotopy Optimization Method (HOM). This method differs from previous homotopy and continuation methods in that its aim is to find a minimizer for each of a set of values of the homotopy parameter, rather than to follow a path of minimizers. We define a second method, called HOPE, by allowing HOM to follow an ensemble of points obtained by per...

Solving efficiently large benchmarks of NP-hard permutation-based problems requires the development of hybrid methods combining different classes of optimization methods. Indeed, it is now acknowledged that such methods perform better than traditional optimization methods when used separately. The key challenge is how to find connections between the divergent search strategies used in each clas...

Discriminative training applied to hidden Markov model (HMM) design can yield significant benefits in recognition accuracy and model compactness. However, compared to Maximum Likelihood based methods, discriminative training typically requires much more computation, as all competing candidates must be considered, not just the correct one. The choice of the algorithm used to optimize the discrim...

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